Prognostic value of platelet levels in patients with aneurysmal Subarachnoid Hemorrhage

Pathophysiological processes following aneurysmal subarachnoid hemorrhage (aSAH) include upregulated underlying systemic inflammation, which is reflected by changes in different peripheral blood cells and their sub-populations. As inflammation is a crucial process that contributes to post-aSAH complications and clincal outcome, blood cell numbers and ratios in systemic circulation may predict the outcome and provide rapid and easy to quantify point of care biomarkers for these critically ill patients. To identify blood-derived cellular inflammatory parameters which allow a precise prediction of patient outcome after aSAH. In this single-center retrospective study, 19 whole blood-derived cellular inflammatory markers and clinical and demographic parameters for 101 aSAH patients were recorded within 24 h after aSAH. Clinical outcome was quantified with modified Rankin scale (mRS) on discharge. Proportional odds logistic regression (POLR) was used to model the patient outcome as the function of clinical parameters and inflammatory markers. The results were validated on a separate hold-out dataset (220 patients). The on-admission platelet count, mean platelet volume (MPV) and mean platelet volume to platelet ratio (MPR) were found to be significant and predictive of patient outcome on discharge. Mean platelet volume (MPV) and mean platelet volume to platelet ratio (MPR) predicted clinical outcome and may serve as easy to quantify point of care biomarker. The findings are potentially relevant for the management of aSAH.


Variable definition and data collection
Patients' demographic and clinical data were recorded at admission.For the exploratory analysis, eighteen CBC-derived inflammation blood markers (Table 3) were selected from the blood analysis performed at the central hospital laboratory.The markers which were found to be significant were used in subsequent confirmatory analysis.SAH was determined by computerized tomography (CT) scan and evidence of an aneurysm was subsequently confirmed by CT angiography or digital subtraction angiography.Patient outcome at discharge was quantified using the Glasgow outcome scale (GOS) and modified Rankin scale (mRS) at discharge (Fig. 1).

Data analysis
The outcome was quantified by modified Rankin scale (mRS) at discharge and summarized in three ordered categories: favorable (0 ≤ mRS ≤ 2), poor (3 ≤ mRS ≤ 5), and death (mRS = 6).The said boundary between favorable and poor outcome is the most common in the literature 15 .Age, sex, the presence of extraventricular drainage (EVD), DCI, pneumonia, or vasospasms, the modified Fisher score, the WFNS score, and all inflammation markers listed in Table 3 were treated as potential predictor variables in proportional odds logistic models.
The WFNS score, which is easily and routinely determined, is known to be highly correlated with or even the most predictive parameter for the patient outcome [16][17][18][19] .In order to enforce our models to outperform it, we decided to use it as a fixed predictor in every model.Next, in the exploratory phase, a series of models with one additional predictor was fitted.Significant predictors (p < 0.05)-i.e.those which improved the simple, WFNSonly model-were recorded.Colinearity between predictors was tested using linear regression.The findings from the exploratory phase were validated on the confirmatory dataset.The dependence between a single predictor variable and the trichotomized outcome was analyzed using one-way ANOVA.If ANOVA indicated significant  www.nature.com/scientificreports/differences between outcome levels, post-hoc t-test was used to identify them.For multiple predictor variables, the dependency was modeled by proportional odds logistic regression (POLR).Multiple regression was performed using all significant predictors found by univariate analysis.All computations were performed in Python (www.python.org), using NumPy 20 , SciPy 21 , and Statsmodels 22 libraries.

Ethical approval
The study was performed according to the guidelines of the Helsinki declaration and was approved by the local ethics committee of the medical faculty of the Heinrich-Heine-University Düsseldorf, Germany (reference number: 2019-787-bio).The informed consent was obtained by the treating neurosurgeon.The manuscript was prepared according to the STROBE guidelines.

Results
In the exploratory phase, WFNS and age were shown to be highly significant predictors for the outcome (p < 0.0001 for both).We also identified platelets, MPV, and MPR as significant predictors (p < 0.05), even when taken as a second independent variable next to the WFNS score.The three markers were strongly linearly related (p < 0.001), which is unsurprising, given that all three are related to platelets.In particular, the inverse of the MPR was nearly perfectly colinear with platelets (R 2 = 0.941).In the confirmatory phase, using a separate dataset, all three markers proved to be significant predictors (POLR, p = 0.009, p = 0.013 and p = 0.011 for platelets, MPV and MPR, respectively).No significant difference in the distribution of the markers was found between the two sets (Table 4).All three markers were also colinear with the WFNS score (p = 0.02, p = 0.04, and p = 0.005) and, in addition, platelets count and MPR were colinear with age (p < 0.0001 and p = 0.0001, respectively).No collinearity between MPV and age was observed (p = 0.4).www.nature.com/scientificreports/However, ANOVA and the post-hoc t-tests showed that these three markers didn't significantly differ over all outcome levels.Platelet levels differed significantly only between patients with favorable outcome and the deceased (p = 0.007), but not between the patients with poor outcome and the other two groups.A similar result was observed for MPV (p = 0.018 for differences between favorable outcome and death), but there was also a trend (p < 0.1) between the groups with favorable and poor outcome.MPR showed the best differentiation capability, with significant differences between favorable on one side and both poor outcome and death on the other (p = 0.042 and p = 0.017, respectively).There was also a statistical trend (p < 0.1) between MPR values for patients with poor outcome and the deceased (Fig. 2a-c).
As age is an important factor for overall health, whose impact rises strongly as it advances, we analyzed the association between the three predictors and the outcome separately for two groups: patients under 60 (133 patients) and patients aged 60 and over (86 patients) (Fig. 3a-f).Although the trends remain the same-decreasing platelet counts and increasing MPV and MPR for worse outcomes-the differences are significant only for MPV for the elderly patients.In part, this can be explained by the reduction in set sizes, as small effects are statistically harder to detect on smaller sets.MPV, however, turns to be a much better predictor in the elderly group than for all patients taken together, with highly significant differences (p < 0.01) between patients with favorable outcome and the other two groups, and a trend (p = 0.076) between unfavorable outcome and the deceased.
In the multiple regression model, using the WFNS score, the age, and all three markers as the predictors, platelets and MPR turned out not to be significant, and a trend (p = 0.07) was observed for MPV.This loss of significance was expected, due to the above collinearities.Figure 4a-d show the ROC curves and the decision curves for predicting favorable outcome and death using the multiple regression model.

Discussion
We investigated several known blood markers involved in inflammation processes, together with clinical parameters, for their predictivity of patient outcome after aSAH.As the number of markers was substantial in relation to the data size, special attention was paid to ensure that the results are reliable and not resulting from random noise in the data.We have therefore reason to believe that our findings are generalizable beyond data used in this study.Patient's initial status, reflected in the WFNS score, is-unsurprisingly-higly predictive of the outcome, confirming the ample evidence reported in the literature.Patients with a low initial WFNS score tend to recover better, while patients with a higher WFNS score or poor clinical status at admission are likely to have a worse outcome.At the same time, the WFNS score is plagued by subjectivity and inaccuracies in early assessment and can be supplemented by laboratory results.The role of platelet count and related indicators, MPV and MPR, needs to be explained within the body of the available knowledge.The current literature on platelets and prediction of post SAH complications is controversial.
While some studies indicate that MPR might serve as a predictor of DCI or overall outcome after aSAH 23,24 , others haven't found such an association 25 .A recent study found no association between platelet count and DCI after aSAH 26 .We applied different ratios of blood cells including mean platelet volume (MPV) and mean platelet volume to platelet ratio (MPR) to evaluate the predictive value of systemic platelet numbers and consequently platelet mediated inflammation.The association between MPR and patient outcome, found in our study, is significant to differentiate and classify different outcome groups (good vs poor outcome) based on modified Rankin scale.Our analysed ratios, however, were not sufficient to differentiate between poor outcome group and the group of patient with in-hospital death.This might be due to high spread of MPR values in the group with poor outcome, causing a significant overlap with the values in the smaller group of deceased patients (Fig. 2c).By its definition, MPR is bound to vary more when the platelet counts are low, and this tends to be the case for patients with poor outcome or death (Fig. 2a), although only the difference between favorable outcome and death was significant.Other studies 27,28 found no significant association between the platelet count and the outcome, but the raw numbers point in the same direction.The latter study found a significant negative correlation between platelets and the SAH volume, suggesting a possible link.
Altogether, our findings suggests that MPR might play a secondary role as the outcome predictor.Nevertheless, combining MPV and MPR can be a valuable tool to rapidly recognize the patients with poor outcome after SAH and hence, may help in treatment decision making during intensive care.

Limitations
The study was retrospective by design, based on a single-center cohort.The primary outcome (mRS) was determined retrospectively based on the medical records.We only investigated mRS at discharge, which is indicative of, but not the same as the long-term outcome, e.g. six months after discharge.Although we used two different datasets for exploration and confirmation, further studies on external and ideally larger cohorts are needed to corroborate the findings.Investigating the distribution of predictor values for different outcome categories showed that log-odds for "death" violate the linearity assumption, thus weakening the predictions.Nevertheless, the overall direction points in the same way as under the linearity assumption.Also, the study was phenomenological and further basic research into the exact mechanisms of inflammation and its consequences is warranted.

Conclusion
Three easy-to-determine inflammation markers, platelet count, MPV, and MPR, are related to patient outcome after suffering aSAH.Together with the WFNS score, they can be used to indicate whether a patient is at a higherthan-expected risk of poor outcome and adjust the treatment as needed.

Figure 1 .
Figure 1.Relationship measures of outcome, modified Rankin scale (mRS) and Glasgow outcome scale (GOS), for the exploratory dataset.Each dot represents a patient.

Figure 2 .
Figure 2. Association between platelet-related markers and the patient outcome (trichotomized mRS score): (a) platelet count; (b) MPV; (c) MPR.Only MPR showed a significant difference between patients with a favorable outcome and both other groups.The difference between poor outcome and death was not significant, but a trend (p < 0.1).

Figure 3 .
Figure 3. Subgroup analysis by age for the platelet-related markers: (a) platelet count; (b) MPV; (c) MPR, all three for patients under 60.Panels (d-f) show the same for patients aged 60 and more.A significant difference can only be observed for MPV in the latter group, but the directions of change for values between outcome groups remain the same.

Figure 4 .
Figure 4. Receiver operating characteristics (ROC), (a) for predicting favorable outcome and (b) for predicting death.Panels (c) and (d) show the respective decision curves.

Table 1 .
Patient demographics, total and by outcome group, for the exploratory and the confirmatory set combined.The entries marked with an asterisk (*) refer only to the exploratory set.

Table 2 .
Patient demographics, total and by dataset used in analysis.p > .05indicates no significant differences between the subsets.

Table 3 .
Marker characteristics for the patients from the exploratory set, total and by outcome group.

Table 4 .
Marker characteristics, total and by dataset used in analysis.p > .05indicates no significant differences between the subsets.